【深度学习】注意力机制(六)

发布时间:2023年12月18日

本文介绍一些注意力机制的实现,包括MobileVITv1/MobileVITv2/DAT/CrossFormer/MOA。

【深度学习】注意力机制(一)

【深度学习】注意力机制(二)

【深度学习】注意力机制(三)

【深度学习】注意力机制(四)

【深度学习】注意力机制(五)

目录

一、MobileVITv1

二、MobileVITv2

三、DAT(Deformable Attention Transformer)

四、CrossFormer

五、MOA(multi-resolution overlapped attention)


一、MobileVITv1

论文地址:https://arxiv.org/pdf/2110.02178v2.pdf

如下图:

该代码块不能直接使用,有相关依赖,可以参考(代码来源):

import math
from typing import Dict, Optional, Sequence, Tuple, Union

import numpy as np
import torch
from torch import Tensor, nn
from torch.nn import functional as F

from cvnets.layers import ConvLayer2d, get_normalization_layer
from cvnets.modules.base_module import BaseModule
from cvnets.modules.transformer import LinearAttnFFN, TransformerEncoder


class MobileViTBlock(BaseModule):
    """
    This class defines the `MobileViT block <https://arxiv.org/abs/2110.02178?context=cs.LG>`_

    Args:
        opts: command line arguments
        in_channels (int): :math:`C_{in}` from an expected input of size :math:`(N, C_{in}, H, W)`
        transformer_dim (int): Input dimension to the transformer unit
        ffn_dim (int): Dimension of the FFN block
        n_transformer_blocks (Optional[int]): Number of transformer blocks. Default: 2
        head_dim (Optional[int]): Head dimension in the multi-head attention. Default: 32
        attn_dropout (Optional[float]): Dropout in multi-head attention. Default: 0.0
        dropout (Optional[float]): Dropout rate. Default: 0.0
        ffn_dropout (Optional[float]): Dropout between FFN layers in transformer. Default: 0.0
        patch_h (Optional[int]): Patch height for unfolding operation. Default: 8
        patch_w (Optional[int]): Patch width for unfolding operation. Default: 8
        transformer_norm_layer (Optional[str]): Normalization layer in the transformer block. Default: layer_norm
        conv_ksize (Optional[int]): Kernel size to learn local representations in MobileViT block. Default: 3
        dilation (Optional[int]): Dilation rate in convolutions. Default: 1
        no_fusion (Optional[bool]): Do not combine the input and output feature maps. Default: False
    """

    def __init__(
        self,
        opts,
        in_channels: int,
        transformer_dim: int,
        ffn_dim: int,
        n_transformer_blocks: Optional[int] = 2,
        head_dim: Optional[int] = 32,
        attn_dropout: Optional[float] = 0.0,
        dropout: Optional[int] = 0.0,
        ffn_dropout: Optional[int] = 0.0,
        patch_h: Optional[int] = 8,
        patch_w: Optional[int] = 8,
        transformer_norm_layer: Optional[str] = "layer_norm",
        conv_ksize: Optional[int] = 3,
        dilation: Optional[int] = 1,
        no_fusion: Optional[bool] = False,
        *args,
        **kwargs
    ) -> None:
        conv_3x3_in = ConvLayer2d(
            opts=opts,
            in_channels=in_channels,
            out_channels=in_channels,
            kernel_size=conv_ksize,
            stride=1,
            use_norm=True,
            use_act=True,
            dilation=dilation,
        )
        conv_1x1_in = ConvLayer2d(
            opts=opts,
            in_channels=in_channels,
            out_channels=transformer_dim,
            kernel_size=1,
            stride=1,
            use_norm=False,
            use_act=False,
        )

        conv_1x1_out = ConvLayer2d(
            opts=opts,
            in_channels=transformer_dim,
            out_channels=in_channels,
            kernel_size=1,
            stride=1,
            use_norm=True,
            use_act=True,
        )
        conv_3x3_out = None
        if not no_fusion:
            conv_3x3_out = ConvLayer2d(
                opts=opts,
                in_channels=2 * in_channels,
                out_channels=in_channels,
                kernel_size=conv_ksize,
                stride=1,
                use_norm=True,
                use_act=True,
            )
        super().__init__()
        self.local_rep = nn.Sequential()
        self.local_rep.add_module(name="conv_3x3", module=conv_3x3_in)
        self.local_rep.add_module(name="conv_1x1", module=conv_1x1_in)

        assert transformer_dim % head_dim == 0
        num_heads = transformer_dim // head_dim

        global_rep = [
            TransformerEncoder(
                opts=opts,
                embed_dim=transformer_dim,
                ffn_latent_dim=ffn_dim,
                num_heads=num_heads,
                attn_dropout=attn_dropout,
                dropout=dropout,
                ffn_dropout=ffn_dropout,
                transformer_norm_layer=transformer_norm_layer,
            )
            for _ in range(n_transformer_blocks)
        ]
        global_rep.append(
            get_normalization_layer(
                opts=opts,
                norm_type=transformer_norm_layer,
                num_features=transformer_dim,
            )
        )
        self.global_rep = nn.Sequential(*global_rep)

        self.conv_proj = conv_1x1_out

        self.fusion = conv_3x3_out

        self.patch_h = patch_h
        self.patch_w = patch_w
        self.patch_area = self.patch_w * self.patch_h

        self.cnn_in_dim = in_channels
        self.cnn_out_dim = transformer_dim
        self.n_heads = num_heads
        self.ffn_dim = ffn_dim
        self.dropout = dropout
        self.attn_dropout = attn_dropout
        self.ffn_dropout = ffn_dropout
        self.dilation = dilation
        self.n_blocks = n_transformer_blocks
        self.conv_ksize = conv_ksize


    def unfolding(self, feature_map: Tensor) -> Tuple[Tensor, Dict]:
        patch_w, patch_h = self.patch_w, self.patch_h
        patch_area = int(patch_w * patch_h)
        batch_size, in_channels, orig_h, orig_w = feature_map.shape

        new_h = int(math.ceil(orig_h / self.patch_h) * self.patch_h)
        new_w = int(math.ceil(orig_w / self.patch_w) * self.patch_w)

        interpolate = False
        if new_w != orig_w or new_h != orig_h:
            # Note: Padding can be done, but then it needs to be handled in attention function.
            feature_map = F.interpolate(
                feature_map, size=(new_h, new_w), mode="bilinear", align_corners=False
            )
            interpolate = True

        # number of patches along width and height
        num_patch_w = new_w // patch_w  # n_w
        num_patch_h = new_h // patch_h  # n_h
        num_patches = num_patch_h * num_patch_w  # N

        # [B, C, H, W] --> [B * C * n_h, p_h, n_w, p_w]
        reshaped_fm = feature_map.reshape(
            batch_size * in_channels * num_patch_h, patch_h, num_patch_w, patch_w
        )
        # [B * C * n_h, p_h, n_w, p_w] --> [B * C * n_h, n_w, p_h, p_w]
        transposed_fm = reshaped_fm.transpose(1, 2)
        # [B * C * n_h, n_w, p_h, p_w] --> [B, C, N, P] where P = p_h * p_w and N = n_h * n_w
        reshaped_fm = transposed_fm.reshape(
            batch_size, in_channels, num_patches, patch_area
        )
        # [B, C, N, P] --> [B, P, N, C]
        transposed_fm = reshaped_fm.transpose(1, 3)
        # [B, P, N, C] --> [BP, N, C]
        patches = transposed_fm.reshape(batch_size * patch_area, num_patches, -1)

        info_dict = {
            "orig_size": (orig_h, orig_w),
            "batch_size": batch_size,
            "interpolate": interpolate,
            "total_patches": num_patches,
            "num_patches_w": num_patch_w,
            "num_patches_h": num_patch_h,
        }

        return patches, info_dict

    def folding(self, patches: Tensor, info_dict: Dict) -> Tensor:
        n_dim = patches.dim()
        assert n_dim == 3, "Tensor should be of shape BPxNxC. Got: {}".format(
            patches.shape
        )
        # [BP, N, C] --> [B, P, N, C]
        patches = patches.contiguous().view(
            info_dict["batch_size"], self.patch_area, info_dict["total_patches"], -1
        )

        batch_size, pixels, num_patches, channels = patches.size()
        num_patch_h = info_dict["num_patches_h"]
        num_patch_w = info_dict["num_patches_w"]

        # [B, P, N, C] --> [B, C, N, P]
        patches = patches.transpose(1, 3)

        # [B, C, N, P] --> [B*C*n_h, n_w, p_h, p_w]
        feature_map = patches.reshape(
            batch_size * channels * num_patch_h, num_patch_w, self.patch_h, self.patch_w
        )
        # [B*C*n_h, n_w, p_h, p_w] --> [B*C*n_h, p_h, n_w, p_w]
        feature_map = feature_map.transpose(1, 2)
        # [B*C*n_h, p_h, n_w, p_w] --> [B, C, H, W]
        feature_map = feature_map.reshape(
            batch_size, channels, num_patch_h * self.patch_h, num_patch_w * self.patch_w
        )
        if info_dict["interpolate"]:
            feature_map = F.interpolate(
                feature_map,
                size=info_dict["orig_size"],
                mode="bilinear",
                align_corners=False,
            )
        return feature_map

    def forward_spatial(self, x: Tensor) -> Tensor:
        res = x

        fm = self.local_rep(x)

        # convert feature map to patches
        patches, info_dict = self.unfolding(fm)

        # learn global representations
        for transformer_layer in self.global_rep:
            patches = transformer_layer(patches)

        # [B x Patch x Patches x C] --> [B x C x Patches x Patch]
        fm = self.folding(patches=patches, info_dict=info_dict)

        fm = self.conv_proj(fm)

        if self.fusion is not None:
            fm = self.fusion(torch.cat((res, fm), dim=1))
        return fm

    def forward_temporal(
        self, x: Tensor, x_prev: Optional[Tensor] = None
    ) -> Union[Tensor, Tuple[Tensor, Tensor]]:

        res = x
        fm = self.local_rep(x)

        # convert feature map to patches
        patches, info_dict = self.unfolding(fm)

        # learn global representations
        for global_layer in self.global_rep:
            if isinstance(global_layer, TransformerEncoder):
                patches = global_layer(x=patches, x_prev=x_prev)
            else:
                patches = global_layer(patches)

        # [B x Patch x Patches x C] --> [B x C x Patches x Patch]
        fm = self.folding(patches=patches, info_dict=info_dict)

        fm = self.conv_proj(fm)

        if self.fusion is not None:
            fm = self.fusion(torch.cat((res, fm), dim=1))
        return fm, patches

    def forward(
        self, x: Union[Tensor, Tuple[Tensor]], *args, **kwargs
    ) -> Union[Tensor, Tuple[Tensor, Tensor]]:
        if isinstance(x, Tuple) and len(x) == 2:
            # for spatio-temporal MobileViT
            return self.forward_temporal(x=x[0], x_prev=x[1])
        elif isinstance(x, Tensor):
            # For image data
            return self.forward_spatial(x)
        else:
            raise NotImplementedError

二、MobileVITv2

论文地址:Separable Self-attention for Mobile Vision Transformers

如下图:

代码不可直接使用,可参考代码来源

class MobileViTBlockv2(BaseModule):
    """
    This class defines the `MobileViTv2 <https://arxiv.org/abs/2206.02680>`_ block

    Args:
        opts: command line arguments
        in_channels (int): :math:`C_{in}` from an expected input of size :math:`(N, C_{in}, H, W)`
        attn_unit_dim (int): Input dimension to the attention unit
        ffn_multiplier (int): Expand the input dimensions by this factor in FFN. Default is 2.
        n_attn_blocks (Optional[int]): Number of attention units. Default: 2
        attn_dropout (Optional[float]): Dropout in multi-head attention. Default: 0.0
        dropout (Optional[float]): Dropout rate. Default: 0.0
        ffn_dropout (Optional[float]): Dropout between FFN layers in transformer. Default: 0.0
        patch_h (Optional[int]): Patch height for unfolding operation. Default: 8
        patch_w (Optional[int]): Patch width for unfolding operation. Default: 8
        conv_ksize (Optional[int]): Kernel size to learn local representations in MobileViT block. Default: 3
        dilation (Optional[int]): Dilation rate in convolutions. Default: 1
        attn_norm_layer (Optional[str]): Normalization layer in the attention block. Default: layer_norm_2d
    """

    def __init__(
        self,
        opts,
        in_channels: int,
        attn_unit_dim: int,
        ffn_multiplier: Optional[Union[Sequence[Union[int, float]], int, float]] = 2.0,
        n_attn_blocks: Optional[int] = 2,
        attn_dropout: Optional[float] = 0.0,
        dropout: Optional[float] = 0.0,
        ffn_dropout: Optional[float] = 0.0,
        patch_h: Optional[int] = 8,
        patch_w: Optional[int] = 8,
        conv_ksize: Optional[int] = 3,
        dilation: Optional[int] = 1,
        attn_norm_layer: Optional[str] = "layer_norm_2d",
        *args,
        **kwargs
    ) -> None:
        cnn_out_dim = attn_unit_dim

        conv_3x3_in = ConvLayer2d(
            opts=opts,
            in_channels=in_channels,
            out_channels=in_channels,
            kernel_size=conv_ksize,
            stride=1,
            use_norm=True,
            use_act=True,
            dilation=dilation,
            groups=in_channels,
        )
        conv_1x1_in = ConvLayer2d(
            opts=opts,
            in_channels=in_channels,
            out_channels=cnn_out_dim,
            kernel_size=1,
            stride=1,
            use_norm=False,
            use_act=False,
        )

        super(MobileViTBlockv2, self).__init__()
        self.local_rep = nn.Sequential(conv_3x3_in, conv_1x1_in)

        self.global_rep, attn_unit_dim = self._build_attn_layer(
            opts=opts,
            d_model=attn_unit_dim,
            ffn_mult=ffn_multiplier,
            n_layers=n_attn_blocks,
            attn_dropout=attn_dropout,
            dropout=dropout,
            ffn_dropout=ffn_dropout,
            attn_norm_layer=attn_norm_layer,
        )

        self.conv_proj = ConvLayer2d(
            opts=opts,
            in_channels=cnn_out_dim,
            out_channels=in_channels,
            kernel_size=1,
            stride=1,
            use_norm=True,
            use_act=False,
        )

        self.patch_h = patch_h
        self.patch_w = patch_w
        self.patch_area = self.patch_w * self.patch_h

        self.cnn_in_dim = in_channels
        self.cnn_out_dim = cnn_out_dim
        self.transformer_in_dim = attn_unit_dim
        self.dropout = dropout
        self.attn_dropout = attn_dropout
        self.ffn_dropout = ffn_dropout
        self.n_blocks = n_attn_blocks
        self.conv_ksize = conv_ksize
        self.enable_coreml_compatible_fn = getattr(
            opts, "common.enable_coreml_compatible_module", False
        )

        if self.enable_coreml_compatible_fn:
            # we set persistent to false so that these weights are not part of model's state_dict
            self.register_buffer(
                name="unfolding_weights",
                tensor=self._compute_unfolding_weights(),
                persistent=False,
            )

    def _compute_unfolding_weights(self) -> Tensor:
        # [P_h * P_w, P_h * P_w]
        weights = torch.eye(self.patch_h * self.patch_w, dtype=torch.float)
        # [P_h * P_w, P_h * P_w] --> [P_h * P_w, 1, P_h, P_w]
        weights = weights.reshape(
            (self.patch_h * self.patch_w, 1, self.patch_h, self.patch_w)
        )
        # [P_h * P_w, 1, P_h, P_w] --> [P_h * P_w * C, 1, P_h, P_w]
        weights = weights.repeat(self.cnn_out_dim, 1, 1, 1)
        return weights

    def _build_attn_layer(
        self,
        opts,
        d_model: int,
        ffn_mult: Union[Sequence, int, float],
        n_layers: int,
        attn_dropout: float,
        dropout: float,
        ffn_dropout: float,
        attn_norm_layer: str,
        *args,
        **kwargs
    ) -> Tuple[nn.Module, int]:

        if isinstance(ffn_mult, Sequence) and len(ffn_mult) == 2:
            ffn_dims = (
                np.linspace(ffn_mult[0], ffn_mult[1], n_layers, dtype=float) * d_model
            )
        elif isinstance(ffn_mult, Sequence) and len(ffn_mult) == 1:
            ffn_dims = [ffn_mult[0] * d_model] * n_layers
        elif isinstance(ffn_mult, (int, float)):
            ffn_dims = [ffn_mult * d_model] * n_layers
        else:
            raise NotImplementedError

        # ensure that dims are multiple of 16
        ffn_dims = [int((d // 16) * 16) for d in ffn_dims]

        global_rep = [
            LinearAttnFFN(
                opts=opts,
                embed_dim=d_model,
                ffn_latent_dim=ffn_dims[block_idx],
                attn_dropout=attn_dropout,
                dropout=dropout,
                ffn_dropout=ffn_dropout,
                norm_layer=attn_norm_layer,
            )
            for block_idx in range(n_layers)
        ]
        global_rep.append(
            get_normalization_layer(
                opts=opts, norm_type=attn_norm_layer, num_features=d_model
            )
        )

        return nn.Sequential(*global_rep), d_model

    def __repr__(self) -> str:
        repr_str = "{}(".format(self.__class__.__name__)

        repr_str += "\n\t Local representations"
        if isinstance(self.local_rep, nn.Sequential):
            for m in self.local_rep:
                repr_str += "\n\t\t {}".format(m)
        else:
            repr_str += "\n\t\t {}".format(self.local_rep)

        repr_str += "\n\t Global representations with patch size of {}x{}".format(
            self.patch_h,
            self.patch_w,
        )
        if isinstance(self.global_rep, nn.Sequential):
            for m in self.global_rep:
                repr_str += "\n\t\t {}".format(m)
        else:
            repr_str += "\n\t\t {}".format(self.global_rep)

        if isinstance(self.conv_proj, nn.Sequential):
            for m in self.conv_proj:
                repr_str += "\n\t\t {}".format(m)
        else:
            repr_str += "\n\t\t {}".format(self.conv_proj)

        repr_str += "\n)"
        return repr_str

    def unfolding_pytorch(self, feature_map: Tensor) -> Tuple[Tensor, Tuple[int, int]]:

        batch_size, in_channels, img_h, img_w = feature_map.shape

        # [B, C, H, W] --> [B, C, P, N]
        patches = F.unfold(
            feature_map,
            kernel_size=(self.patch_h, self.patch_w),
            stride=(self.patch_h, self.patch_w),
        )
        patches = patches.reshape(
            batch_size, in_channels, self.patch_h * self.patch_w, -1
        )

        return patches, (img_h, img_w)

    def folding_pytorch(self, patches: Tensor, output_size: Tuple[int, int]) -> Tensor:
        batch_size, in_dim, patch_size, n_patches = patches.shape

        # [B, C, P, N]
        patches = patches.reshape(batch_size, in_dim * patch_size, n_patches)

        feature_map = F.fold(
            patches,
            output_size=output_size,
            kernel_size=(self.patch_h, self.patch_w),
            stride=(self.patch_h, self.patch_w),
        )

        return feature_map

    def unfolding_coreml(self, feature_map: Tensor) -> Tuple[Tensor, Tuple[int, int]]:
        # im2col is not implemented in Coreml, so here we hack its implementation using conv2d
        # we compute the weights

        # [B, C, H, W] --> [B, C, P, N]
        batch_size, in_channels, img_h, img_w = feature_map.shape
        #
        patches = F.conv2d(
            feature_map,
            self.unfolding_weights,
            bias=None,
            stride=(self.patch_h, self.patch_w),
            padding=0,
            dilation=1,
            groups=in_channels,
        )
        patches = patches.reshape(
            batch_size, in_channels, self.patch_h * self.patch_w, -1
        )
        return patches, (img_h, img_w)

    def folding_coreml(self, patches: Tensor, output_size: Tuple[int, int]) -> Tensor:
        # col2im is not supported on coreml, so tracing fails
        # We hack folding function via pixel_shuffle to enable coreml tracing
        batch_size, in_dim, patch_size, n_patches = patches.shape

        n_patches_h = output_size[0] // self.patch_h
        n_patches_w = output_size[1] // self.patch_w

        feature_map = patches.reshape(
            batch_size, in_dim * self.patch_h * self.patch_w, n_patches_h, n_patches_w
        )
        assert (
            self.patch_h == self.patch_w
        ), "For Coreml, we need patch_h and patch_w are the same"
        feature_map = F.pixel_shuffle(feature_map, upscale_factor=self.patch_h)
        return feature_map

    def resize_input_if_needed(self, x):
        batch_size, in_channels, orig_h, orig_w = x.shape
        if orig_h % self.patch_h != 0 or orig_w % self.patch_w != 0:
            new_h = int(math.ceil(orig_h / self.patch_h) * self.patch_h)
            new_w = int(math.ceil(orig_w / self.patch_w) * self.patch_w)
            x = F.interpolate(
                x, size=(new_h, new_w), mode="bilinear", align_corners=True
            )
        return x

    def forward_spatial(self, x: Tensor, *args, **kwargs) -> Tensor:
        x = self.resize_input_if_needed(x)

        fm = self.local_rep(x)

        # convert feature map to patches
        if self.enable_coreml_compatible_fn:
            patches, output_size = self.unfolding_coreml(fm)
        else:
            patches, output_size = self.unfolding_pytorch(fm)

        # learn global representations on all patches
        patches = self.global_rep(patches)

        # [B x Patch x Patches x C] --> [B x C x Patches x Patch]
        if self.enable_coreml_compatible_fn:
            fm = self.folding_coreml(patches=patches, output_size=output_size)
        else:
            fm = self.folding_pytorch(patches=patches, output_size=output_size)
        fm = self.conv_proj(fm)

        return fm

    def forward_temporal(
        self, x: Tensor, x_prev: Tensor, *args, **kwargs
    ) -> Union[Tensor, Tuple[Tensor, Tensor]]:
        x = self.resize_input_if_needed(x)

        fm = self.local_rep(x)

        # convert feature map to patches
        if self.enable_coreml_compatible_fn:
            patches, output_size = self.unfolding_coreml(fm)
        else:
            patches, output_size = self.unfolding_pytorch(fm)

        # learn global representations
        for global_layer in self.global_rep:
            if isinstance(global_layer, LinearAttnFFN):
                patches = global_layer(x=patches, x_prev=x_prev)
            else:
                patches = global_layer(patches)

        # [B x Patch x Patches x C] --> [B x C x Patches x Patch]
        if self.enable_coreml_compatible_fn:
            fm = self.folding_coreml(patches=patches, output_size=output_size)
        else:
            fm = self.folding_pytorch(patches=patches, output_size=output_size)
        fm = self.conv_proj(fm)

        return fm, patches

    def forward(
        self, x: Union[Tensor, Tuple[Tensor]], *args, **kwargs
    ) -> Union[Tensor, Tuple[Tensor, Tensor]]:
        if isinstance(x, Tuple) and len(x) == 2:
            # for spatio-temporal data (e.g., videos)
            return self.forward_temporal(x=x[0], x_prev=x[1])
        elif isinstance(x, Tensor):
            # for image data
            return self.forward_spatial(x)
        else:
            raise NotImplementedError

三、DAT(Deformable Attention Transformer)

论文地址:Vision Transformer with Deformable Attention

如下图:

代码如下(代码来源):

class DAttentionBaseline(nn.Module):

    def __init__(
        self, q_size, kv_size, n_heads, n_head_channels, n_groups,
        attn_drop, proj_drop, stride, 
        offset_range_factor, use_pe, dwc_pe,
        no_off, fixed_pe, ksize, log_cpb
    ):

        super().__init__()
        self.dwc_pe = dwc_pe
        self.n_head_channels = n_head_channels
        self.scale = self.n_head_channels ** -0.5
        self.n_heads = n_heads
        self.q_h, self.q_w = q_size
        # self.kv_h, self.kv_w = kv_size
        self.kv_h, self.kv_w = self.q_h // stride, self.q_w // stride
        self.nc = n_head_channels * n_heads
        self.n_groups = n_groups
        self.n_group_channels = self.nc // self.n_groups
        self.n_group_heads = self.n_heads // self.n_groups
        self.use_pe = use_pe
        self.fixed_pe = fixed_pe
        self.no_off = no_off
        self.offset_range_factor = offset_range_factor
        self.ksize = ksize
        self.log_cpb = log_cpb
        self.stride = stride
        kk = self.ksize
        pad_size = kk // 2 if kk != stride else 0

        self.conv_offset = nn.Sequential(
            nn.Conv2d(self.n_group_channels, self.n_group_channels, kk, stride, pad_size, groups=self.n_group_channels),
            LayerNormProxy(self.n_group_channels),
            nn.GELU(),
            nn.Conv2d(self.n_group_channels, 2, 1, 1, 0, bias=False)
        )
        if self.no_off:
            for m in self.conv_offset.parameters():
                m.requires_grad_(False)

        self.proj_q = nn.Conv2d(
            self.nc, self.nc,
            kernel_size=1, stride=1, padding=0
        )

        self.proj_k = nn.Conv2d(
            self.nc, self.nc,
            kernel_size=1, stride=1, padding=0
        )

        self.proj_v = nn.Conv2d(
            self.nc, self.nc,
            kernel_size=1, stride=1, padding=0
        )

        self.proj_out = nn.Conv2d(
            self.nc, self.nc,
            kernel_size=1, stride=1, padding=0
        )

        self.proj_drop = nn.Dropout(proj_drop, inplace=True)
        self.attn_drop = nn.Dropout(attn_drop, inplace=True)

        if self.use_pe and not self.no_off:
            if self.dwc_pe:
                self.rpe_table = nn.Conv2d(
                    self.nc, self.nc, kernel_size=3, stride=1, padding=1, groups=self.nc)
            elif self.fixed_pe:
                self.rpe_table = nn.Parameter(
                    torch.zeros(self.n_heads, self.q_h * self.q_w, self.kv_h * self.kv_w)
                )
                trunc_normal_(self.rpe_table, std=0.01)
            elif self.log_cpb:
                # Borrowed from Swin-V2
                self.rpe_table = nn.Sequential(
                    nn.Linear(2, 32, bias=True),
                    nn.ReLU(inplace=True),
                    nn.Linear(32, self.n_group_heads, bias=False)
                )
            else:
                self.rpe_table = nn.Parameter(
                    torch.zeros(self.n_heads, self.q_h * 2 - 1, self.q_w * 2 - 1)
                )
                trunc_normal_(self.rpe_table, std=0.01)
        else:
            self.rpe_table = None

    @torch.no_grad()
    def _get_ref_points(self, H_key, W_key, B, dtype, device):

        ref_y, ref_x = torch.meshgrid(
            torch.linspace(0.5, H_key - 0.5, H_key, dtype=dtype, device=device),
            torch.linspace(0.5, W_key - 0.5, W_key, dtype=dtype, device=device),
            indexing='ij'
        )
        ref = torch.stack((ref_y, ref_x), -1)
        ref[..., 1].div_(W_key - 1.0).mul_(2.0).sub_(1.0)
        ref[..., 0].div_(H_key - 1.0).mul_(2.0).sub_(1.0)
        ref = ref[None, ...].expand(B * self.n_groups, -1, -1, -1) # B * g H W 2

        return ref
    
    @torch.no_grad()
    def _get_q_grid(self, H, W, B, dtype, device):

        ref_y, ref_x = torch.meshgrid(
            torch.arange(0, H, dtype=dtype, device=device),
            torch.arange(0, W, dtype=dtype, device=device),
            indexing='ij'
        )
        ref = torch.stack((ref_y, ref_x), -1)
        ref[..., 1].div_(W - 1.0).mul_(2.0).sub_(1.0)
        ref[..., 0].div_(H - 1.0).mul_(2.0).sub_(1.0)
        ref = ref[None, ...].expand(B * self.n_groups, -1, -1, -1) # B * g H W 2

        return ref

    def forward(self, x):

        B, C, H, W = x.size()
        dtype, device = x.dtype, x.device

        q = self.proj_q(x)
        q_off = einops.rearrange(q, 'b (g c) h w -> (b g) c h w', g=self.n_groups, c=self.n_group_channels)
        offset = self.conv_offset(q_off).contiguous()  # B * g 2 Hg Wg
        Hk, Wk = offset.size(2), offset.size(3)
        n_sample = Hk * Wk

        if self.offset_range_factor >= 0 and not self.no_off:
            offset_range = torch.tensor([1.0 / (Hk - 1.0), 1.0 / (Wk - 1.0)], device=device).reshape(1, 2, 1, 1)
            offset = offset.tanh().mul(offset_range).mul(self.offset_range_factor)

        offset = einops.rearrange(offset, 'b p h w -> b h w p')
        reference = self._get_ref_points(Hk, Wk, B, dtype, device)

        if self.no_off:
            offset = offset.fill_(0.0)

        if self.offset_range_factor >= 0:
            pos = offset + reference
        else:
            pos = (offset + reference).clamp(-1., +1.)

        if self.no_off:
            x_sampled = F.avg_pool2d(x, kernel_size=self.stride, stride=self.stride)
            assert x_sampled.size(2) == Hk and x_sampled.size(3) == Wk, f"Size is {x_sampled.size()}"
        else:
            x_sampled = F.grid_sample(
                input=x.reshape(B * self.n_groups, self.n_group_channels, H, W), 
                grid=pos[..., (1, 0)], # y, x -> x, y
                mode='bilinear', align_corners=True) # B * g, Cg, Hg, Wg
                

        x_sampled = x_sampled.reshape(B, C, 1, n_sample)

        q = q.reshape(B * self.n_heads, self.n_head_channels, H * W)
        k = self.proj_k(x_sampled).reshape(B * self.n_heads, self.n_head_channels, n_sample)
        v = self.proj_v(x_sampled).reshape(B * self.n_heads, self.n_head_channels, n_sample)

        attn = torch.einsum('b c m, b c n -> b m n', q, k) # B * h, HW, Ns
        attn = attn.mul(self.scale)

        if self.use_pe and (not self.no_off):

            if self.dwc_pe:
                residual_lepe = self.rpe_table(q.reshape(B, C, H, W)).reshape(B * self.n_heads, self.n_head_channels, H * W)
            elif self.fixed_pe:
                rpe_table = self.rpe_table
                attn_bias = rpe_table[None, ...].expand(B, -1, -1, -1)
                attn = attn + attn_bias.reshape(B * self.n_heads, H * W, n_sample)
            elif self.log_cpb:
                q_grid = self._get_q_grid(H, W, B, dtype, device)
                displacement = (q_grid.reshape(B * self.n_groups, H * W, 2).unsqueeze(2) - pos.reshape(B * self.n_groups, n_sample, 2).unsqueeze(1)).mul(4.0) # d_y, d_x [-8, +8]
                displacement = torch.sign(displacement) * torch.log2(torch.abs(displacement) + 1.0) / np.log2(8.0)
                attn_bias = self.rpe_table(displacement) # B * g, H * W, n_sample, h_g
                attn = attn + einops.rearrange(attn_bias, 'b m n h -> (b h) m n', h=self.n_group_heads)
            else:
                rpe_table = self.rpe_table
                rpe_bias = rpe_table[None, ...].expand(B, -1, -1, -1)
                q_grid = self._get_q_grid(H, W, B, dtype, device)
                displacement = (q_grid.reshape(B * self.n_groups, H * W, 2).unsqueeze(2) - pos.reshape(B * self.n_groups, n_sample, 2).unsqueeze(1)).mul(0.5)
                attn_bias = F.grid_sample(
                    input=einops.rearrange(rpe_bias, 'b (g c) h w -> (b g) c h w', c=self.n_group_heads, g=self.n_groups),
                    grid=displacement[..., (1, 0)],
                    mode='bilinear', align_corners=True) # B * g, h_g, HW, Ns

                attn_bias = attn_bias.reshape(B * self.n_heads, H * W, n_sample)
                attn = attn + attn_bias

        attn = F.softmax(attn, dim=2)
        attn = self.attn_drop(attn)

        out = torch.einsum('b m n, b c n -> b c m', attn, v)

        if self.use_pe and self.dwc_pe:
            out = out + residual_lepe
        out = out.reshape(B, C, H, W)

        y = self.proj_drop(self.proj_out(out))

        return y, pos.reshape(B, self.n_groups, Hk, Wk, 2), reference.reshape(B, self.n_groups, Hk, Wk, 2)

四、CrossFormer

该论文有好几个模块论文地址:CROSSFORMER: A VERSATILE VISION TRANSFORMER HINGING ON CROSS-SCALE ATTENTION

SDA、LDA、DPB如下图:

网络结构如下图:

代码如下(代码来源):

import torch
import torch.nn as nn
import torch.utils.checkpoint as checkpoint
from timm.models.layers import DropPath, to_2tuple, trunc_normal_


class Mlp(nn.Module):
    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x

class DynamicPosBias(nn.Module):
    def __init__(self, dim, num_heads, residual):
        super().__init__()
        self.residual = residual
        self.num_heads = num_heads
        self.pos_dim = dim // 4
        self.pos_proj = nn.Linear(2, self.pos_dim)
        self.pos1 = nn.Sequential(
            nn.LayerNorm(self.pos_dim),
            nn.ReLU(inplace=True),
            nn.Linear(self.pos_dim, self.pos_dim),
        )
        self.pos2 = nn.Sequential(
            nn.LayerNorm(self.pos_dim),
            nn.ReLU(inplace=True),
            nn.Linear(self.pos_dim, self.pos_dim)
        )
        self.pos3 = nn.Sequential(
            nn.LayerNorm(self.pos_dim),
            nn.ReLU(inplace=True),
            nn.Linear(self.pos_dim, self.num_heads)
        )
    def forward(self, biases):
        if self.residual:
            pos = self.pos_proj(biases) # 2Wh-1 * 2Ww-1, heads
            pos = pos + self.pos1(pos)
            pos = pos + self.pos2(pos)
            pos = self.pos3(pos)
        else:
            pos = self.pos3(self.pos2(self.pos1(self.pos_proj(biases))))
        return pos

    def flops(self, N):
        flops = N * 2 * self.pos_dim
        flops += N * self.pos_dim * self.pos_dim
        flops += N * self.pos_dim * self.pos_dim
        flops += N * self.pos_dim * self.num_heads
        return flops

class Attention(nn.Module):
    r""" Multi-head self attention module with dynamic position bias.

    Args:
        dim (int): Number of input channels.
        group_size (tuple[int]): The height and width of the group.
        num_heads (int): Number of attention heads.
        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
    """

    def __init__(self, dim, group_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.,
                 position_bias=True):

        super().__init__()
        self.dim = dim
        self.group_size = group_size  # Wh, Ww
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim ** -0.5
        self.position_bias = position_bias

        if position_bias:
            self.pos = DynamicPosBias(self.dim // 4, self.num_heads, residual=False)
            
            # generate mother-set
            position_bias_h = torch.arange(1 - self.group_size[0], self.group_size[0])
            position_bias_w = torch.arange(1 - self.group_size[1], self.group_size[1])
            biases = torch.stack(torch.meshgrid([position_bias_h, position_bias_w]))  # 2, 2Wh-1, 2W2-1
            biases = biases.flatten(1).transpose(0, 1).float()
            self.register_buffer("biases", biases)

            # get pair-wise relative position index for each token inside the group
            coords_h = torch.arange(self.group_size[0])
            coords_w = torch.arange(self.group_size[1])
            coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
            coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
            relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
            relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
            relative_coords[:, :, 0] += self.group_size[0] - 1  # shift to start from 0
            relative_coords[:, :, 1] += self.group_size[1] - 1
            relative_coords[:, :, 0] *= 2 * self.group_size[1] - 1
            relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
            self.register_buffer("relative_position_index", relative_position_index)

        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

        self.softmax = nn.Softmax(dim=-1)

    def forward(self, x, mask=None):
        """
        Args:
            x: input features with shape of (num_groups*B, N, C)
            mask: (0/-inf) mask with shape of (num_groups, Wh*Ww, Wh*Ww) or None
        """
        B_, N, C = x.shape
        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)

        q = q * self.scale
        attn = (q @ k.transpose(-2, -1))

        if self.position_bias:
            pos = self.pos(self.biases) # 2Wh-1 * 2Ww-1, heads
            # select position bias
            relative_position_bias = pos[self.relative_position_index.view(-1)].view(
                self.group_size[0] * self.group_size[1], self.group_size[0] * self.group_size[1], -1)  # Wh*Ww,Wh*Ww,nH
            relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
            attn = attn + relative_position_bias.unsqueeze(0)

        if mask is not None:
            nW = mask.shape[0]
            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
            attn = attn.view(-1, self.num_heads, N, N)
            attn = self.softmax(attn)
        else:
            attn = self.softmax(attn)

        attn = self.attn_drop(attn)

        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x

    def extra_repr(self) -> str:
        return f'dim={self.dim}, group_size={self.group_size}, num_heads={self.num_heads}'

    def flops(self, N):
        # calculate flops for 1 group with token length of N
        flops = 0
        # qkv = self.qkv(x)
        flops += N * self.dim * 3 * self.dim
        # attn = (q @ k.transpose(-2, -1))
        flops += self.num_heads * N * (self.dim // self.num_heads) * N
        #  x = (attn @ v)
        flops += self.num_heads * N * N * (self.dim // self.num_heads)
        # x = self.proj(x)
        flops += N * self.dim * self.dim
        if self.position_bias:
            flops += self.pos.flops(N)
        return flops


class CrossFormerBlock(nn.Module):
    r""" CrossFormer Block.

    Args:
        dim (int): Number of input channels.
        input_resolution (tuple[int]): Input resulotion.
        num_heads (int): Number of attention heads.
        group_size (int): Group size.
        lsda_flag (int): use SDA or LDA, 0 for SDA and 1 for LDA.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
        drop (float, optional): Dropout rate. Default: 0.0
        attn_drop (float, optional): Attention dropout rate. Default: 0.0
        drop_path (float, optional): Stochastic depth rate. Default: 0.0
        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
    """

    def __init__(self, dim, input_resolution, num_heads, group_size=7, lsda_flag=0,
                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
                 act_layer=nn.GELU, norm_layer=nn.LayerNorm, num_patch_size=1):
        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.num_heads = num_heads
        self.group_size = group_size
        self.lsda_flag = lsda_flag
        self.mlp_ratio = mlp_ratio
        self.num_patch_size = num_patch_size
        if min(self.input_resolution) <= self.group_size:
            # if group size is larger than input resolution, we don't partition groups
            self.lsda_flag = 0
            self.group_size = min(self.input_resolution)

        self.norm1 = norm_layer(dim)

        self.attn = Attention(
            dim, group_size=to_2tuple(self.group_size), num_heads=num_heads,
            qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop,
            position_bias=True)

        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)

        attn_mask = None
        self.register_buffer("attn_mask", attn_mask)

    def forward(self, x):
        H, W = self.input_resolution
        B, L, C = x.shape
        assert L == H * W, "input feature has wrong size %d, %d, %d" % (L, H, W)

        shortcut = x
        x = self.norm1(x)
        x = x.view(B, H, W, C)

        # group embeddings
        G = self.group_size
        if self.lsda_flag == 0: # 0 for SDA
            x = x.reshape(B, H // G, G, W // G, G, C).permute(0, 1, 3, 2, 4, 5)
        else: # 1 for LDA
            x = x.reshape(B, G, H // G, G, W // G, C).permute(0, 2, 4, 1, 3, 5)
        x = x.reshape(B * H * W // G**2, G**2, C)

        # multi-head self-attention
        x = self.attn(x, mask=self.attn_mask)  # nW*B, G*G, C

        # ungroup embeddings
        x = x.reshape(B, H // G, W // G, G, G, C)
        if self.lsda_flag == 0:
            x = x.permute(0, 1, 3, 2, 4, 5).reshape(B, H, W, C)
        else:
            x = x.permute(0, 3, 1, 4, 2, 5).reshape(B, H, W, C)
        x = x.view(B, H * W, C)

        # FFN
        x = shortcut + self.drop_path(x)
        x = x + self.drop_path(self.mlp(self.norm2(x)))

        return x

    def extra_repr(self) -> str:
        return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
               f"group_size={self.group_size}, lsda_flag={self.lsda_flag}, mlp_ratio={self.mlp_ratio}"

    def flops(self):
        flops = 0
        H, W = self.input_resolution
        # norm1
        flops += self.dim * H * W
        # LSDA
        nW = H * W / self.group_size / self.group_size
        flops += nW * self.attn.flops(self.group_size * self.group_size)
        # mlp
        flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
        # norm2
        flops += self.dim * H * W
        return flops

class PatchMerging(nn.Module):
    r""" Patch Merging Layer.

    Args:
        input_resolution (tuple[int]): Resolution of input feature.
        dim (int): Number of input channels.
        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
    """

    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm, patch_size=[2], num_input_patch_size=1):
        super().__init__()
        self.input_resolution = input_resolution
        self.dim = dim
        self.reductions = nn.ModuleList()
        self.patch_size = patch_size
        self.norm = norm_layer(dim)

        for i, ps in enumerate(patch_size):
            if i == len(patch_size) - 1:
                out_dim = 2 * dim // 2 ** i
            else:
                out_dim = 2 * dim // 2 ** (i + 1)
            stride = 2
            padding = (ps - stride) // 2
            self.reductions.append(nn.Conv2d(dim, out_dim, kernel_size=ps, 
                                                stride=stride, padding=padding))

    def forward(self, x):
        """
        x: B, H*W, C
        """
        H, W = self.input_resolution
        B, L, C = x.shape
        assert L == H * W, "input feature has wrong size"
        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."

        x = self.norm(x)
        x = x.view(B, H, W, C).permute(0, 3, 1, 2)

        xs = []
        for i in range(len(self.reductions)):
            tmp_x = self.reductions[i](x).flatten(2).transpose(1, 2)
            xs.append(tmp_x)
        x = torch.cat(xs, dim=2)
        return x

    def extra_repr(self) -> str:
        return f"input_resolution={self.input_resolution}, dim={self.dim}"

    def flops(self):
        H, W = self.input_resolution
        flops = H * W * self.dim
        for i, ps in enumerate(self.patch_size):
            if i == len(self.patch_size) - 1:
                out_dim = 2 * self.dim // 2 ** i
            else:
                out_dim = 2 * self.dim // 2 ** (i + 1)
            flops += (H // 2) * (W // 2) * ps * ps * out_dim * self.dim
        return flops


class Stage(nn.Module):
    """ CrossFormer blocks for one stage.

    Args:
        dim (int): Number of input channels.
        input_resolution (tuple[int]): Input resolution.
        depth (int): Number of blocks.
        num_heads (int): Number of attention heads.
        group_size (int): variable G in the paper, one group has GxG embeddings
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
        drop (float, optional): Dropout rate. Default: 0.0
        attn_drop (float, optional): Attention dropout rate. Default: 0.0
        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
    """

    def __init__(self, dim, input_resolution, depth, num_heads, group_size,
                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
                 patch_size_end=[4], num_patch_size=None):

        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.depth = depth
        self.use_checkpoint = use_checkpoint

        # build blocks
        self.blocks = nn.ModuleList()
        for i in range(depth):
            lsda_flag = 0 if (i % 2 == 0) else 1
            self.blocks.append(CrossFormerBlock(dim=dim, input_resolution=input_resolution,
                                 num_heads=num_heads, group_size=group_size,
                                 lsda_flag=lsda_flag,
                                 mlp_ratio=mlp_ratio,
                                 qkv_bias=qkv_bias, qk_scale=qk_scale,
                                 drop=drop, attn_drop=attn_drop,
                                 drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
                                 norm_layer=norm_layer,
                                 num_patch_size=num_patch_size))

        # patch merging layer
        if downsample is not None:
            self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer, 
                                         patch_size=patch_size_end, num_input_patch_size=num_patch_size)
        else:
            self.downsample = None

    def forward(self, x):
        for blk in self.blocks:
            if self.use_checkpoint:
                x = checkpoint.checkpoint(blk, x)
            else:
                x = blk(x)
        if self.downsample is not None:
            x = self.downsample(x)
        return x

    def extra_repr(self) -> str:
        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"

    def flops(self):
        flops = 0
        for blk in self.blocks:
            flops += blk.flops()
        if self.downsample is not None:
            flops += self.downsample.flops()
        return flops


class PatchEmbed(nn.Module):
    r""" Image to Patch Embedding

    Args:
        img_size (int): Image size.  Default: 224.
        patch_size (int): Patch token size. Default: [4].
        in_chans (int): Number of input image channels. Default: 3.
        embed_dim (int): Number of linear projection output channels. Default: 96.
        norm_layer (nn.Module, optional): Normalization layer. Default: None
    """

    def __init__(self, img_size=224, patch_size=[4], in_chans=3, embed_dim=96, norm_layer=None):
        super().__init__()
        img_size = to_2tuple(img_size)
        # patch_size = to_2tuple(patch_size)
        patches_resolution = [img_size[0] // patch_size[0], img_size[0] // patch_size[0]]
        self.img_size = img_size
        self.patch_size = patch_size
        self.patches_resolution = patches_resolution
        self.num_patches = patches_resolution[0] * patches_resolution[1]

        self.in_chans = in_chans
        self.embed_dim = embed_dim

        self.projs = nn.ModuleList()
        for i, ps in enumerate(patch_size):
            if i == len(patch_size) - 1:
                dim = embed_dim // 2 ** i
            else:
                dim = embed_dim // 2 ** (i + 1)
            stride = patch_size[0]
            padding = (ps - patch_size[0]) // 2
            self.projs.append(nn.Conv2d(in_chans, dim, kernel_size=ps, stride=stride, padding=padding))
        if norm_layer is not None:
            self.norm = norm_layer(embed_dim)
        else:
            self.norm = None

    def forward(self, x):
        B, C, H, W = x.shape
        # FIXME look at relaxing size constraints
        assert H == self.img_size[0] and W == self.img_size[1], \
            f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
        xs = []
        for i in range(len(self.projs)):
            tx = self.projs[i](x).flatten(2).transpose(1, 2)
            xs.append(tx)  # B Ph*Pw C
        x = torch.cat(xs, dim=2)
        if self.norm is not None:
            x = self.norm(x)
        return x

    def flops(self):
        Ho, Wo = self.patches_resolution
        flops = 0
        for i, ps in enumerate(self.patch_size):
            if i == len(self.patch_size) - 1:
                dim = self.embed_dim // 2 ** i
            else:
                dim = self.embed_dim // 2 ** (i + 1)
            flops += Ho * Wo * dim * self.in_chans * (self.patch_size[i] * self.patch_size[i])
        if self.norm is not None:
            flops += Ho * Wo * self.embed_dim
        return flops


class CrossFormer(nn.Module):
    r""" CrossFormer
        A PyTorch impl of : `CrossFormer: A Versatile Vision Transformer Based on Cross-scale Attention`  -

    Args:
        img_size (int | tuple(int)): Input image size. Default 224
        patch_size (int | tuple(int)): Patch size. Default: 4
        in_chans (int): Number of input image channels. Default: 3
        num_classes (int): Number of classes for classification head. Default: 1000
        embed_dim (int): Patch embedding dimension. Default: 96
        depths (tuple(int)): Depth of each stage.
        num_heads (tuple(int)): Number of attention heads in different layers.
        group_size (int): Group size. Default: 7
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
        drop_rate (float): Dropout rate. Default: 0
        attn_drop_rate (float): Attention dropout rate. Default: 0
        drop_path_rate (float): Stochastic depth rate. Default: 0.1
        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
        patch_norm (bool): If True, add normalization after patch embedding. Default: True
        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
    """

    def __init__(self, img_size=224, patch_size=[4], in_chans=3, num_classes=1000,
                 embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24],
                 group_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,
                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
                 norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
                 use_checkpoint=False, merge_size=[[2], [2], [2]], **kwargs):
        super().__init__()

        self.num_classes = num_classes
        self.num_layers = len(depths)
        self.embed_dim = embed_dim
        self.ape = ape
        self.patch_norm = patch_norm
        self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
        self.mlp_ratio = mlp_ratio

        # split image into non-overlapping patches
        self.patch_embed = PatchEmbed(
            img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,
            norm_layer=norm_layer if self.patch_norm else None)
        num_patches = self.patch_embed.num_patches
        patches_resolution = self.patch_embed.patches_resolution
        self.patches_resolution = patches_resolution

        # absolute position embedding
        if self.ape:
            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
            trunc_normal_(self.absolute_pos_embed, std=.02)

        self.pos_drop = nn.Dropout(p=drop_rate)

        # stochastic depth
        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule

        # build layers
        self.layers = nn.ModuleList()

        num_patch_sizes = [len(patch_size)] + [len(m) for m in merge_size]
        for i_layer in range(self.num_layers):
            patch_size_end = merge_size[i_layer] if i_layer < self.num_layers - 1 else None
            num_patch_size = num_patch_sizes[i_layer]
            layer = Stage(dim=int(embed_dim * 2 ** i_layer),
                               input_resolution=(patches_resolution[0] // (2 ** i_layer),
                                                 patches_resolution[1] // (2 ** i_layer)),
                               depth=depths[i_layer],
                               num_heads=num_heads[i_layer],
                               group_size=group_size[i_layer],
                               mlp_ratio=self.mlp_ratio,
                               qkv_bias=qkv_bias, qk_scale=qk_scale,
                               drop=drop_rate, attn_drop=attn_drop_rate,
                               drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
                               norm_layer=norm_layer,
                               downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
                               use_checkpoint=use_checkpoint,
                               patch_size_end=patch_size_end,
                               num_patch_size=num_patch_size)
            self.layers.append(layer)

        self.norm = norm_layer(self.num_features)
        self.avgpool = nn.AdaptiveAvgPool1d(1)
        self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()

        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)

    @torch.jit.ignore
    def no_weight_decay(self):
        return {'absolute_pos_embed'}

    @torch.jit.ignore
    def no_weight_decay_keywords(self):
        return {'relative_position_bias_table'}

    def forward_features(self, x):
        x = self.patch_embed(x)
        if self.ape:
            x = x + self.absolute_pos_embed
        x = self.pos_drop(x)

        for layer in self.layers:
            x = layer(x)

        x = self.norm(x)  # B L C
        x = self.avgpool(x.transpose(1, 2))  # B C 1
        x = torch.flatten(x, 1)
        return x

    def forward(self, x):
        x = self.forward_features(x)
        x = self.head(x)
        return x

    def flops(self):
        flops = 0
        flops += self.patch_embed.flops()
        for i, layer in enumerate(self.layers):
            flops += layer.flops()
        flops += self.num_features * self.patches_resolution[0] * self.patches_resolution[1] // (2 ** self.num_layers)
        flops += self.num_features * self.num_classes
        return flops

五、MOA(multi-resolution overlapped attention)

论文地址:Aggregating Global Features into Local Vision Transformer

如下图:

代码如下(代码来源):


# --------------------------------------------------------
# Adopted from Swin Transformer
# Modified by Krushi Patel
# --------------------------------------------------------

import torch
import torch.nn as nn
import torch.utils.checkpoint as checkpoint
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from einops.layers.torch import Rearrange, Reduce

class Mlp(nn.Module):
    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x


def window_partition(x, window_size):
    """
    Args:
        x: (B, H, W, C)
        window_size (int): window size

    Returns:
        windows: (num_windows*B, window_size, window_size, C)
    """
    B, H, W, C = x.shape
    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
    return windows





def window_reverse(windows, window_size, H, W):
    """
    Args:
        windows: (num_windows*B, window_size, window_size, C)
        window_size (int): Window size
        H (int): Height of image
        W (int): Width of image

    Returns:
        x: (B, H, W, C)
    """
    B = int(windows.shape[0] / (H * W / window_size / window_size))
    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
    return x


class WindowAttention(nn.Module):
    r""" Window based multi-head self attention (W-MSA) module with relative position bias.
    It supports both of shifted and non-shifted window.

    Args:
        dim (int): Number of input channels.
        window_size (tuple[int]): The height and width of the window.
        num_heads (int): Number of attention heads.
        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
    """

    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):

        super().__init__()
        self.dim = dim
        self.window_size = window_size  # Wh, Ww
        self.query_size = self.window_size
        self.key_size = self.window_size[0] * 2
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim ** -0.5
        
        # define a parameter table of relative position bias
        self.relative_position_bias_table = nn.Parameter(
            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))  # 2*Wh-1 * 2*Ww-1, nH
        
        # get pair-wise relative position index for each token inside the window
        coords_h = torch.arange(self.window_size[0])
        coords_w = torch.arange(self.window_size[1])
        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
        
        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0

        relative_coords[:, :, 1] += self.window_size[1] - 1
        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
        
        self.register_buffer("relative_position_index", relative_position_index)

        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

        trunc_normal_(self.relative_position_bias_table, std=.02)
        self.softmax = nn.Softmax(dim=-1)

    def forward(self, x):
        """
        Args:
            x: input features with shape of (num_windows*B, N, C)
            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
        """
        B_, N, C = x.shape
        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)

        q = q * self.scale
        attn = (q @ k.transpose(-2, -1))

        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww

        
        attn = attn + relative_position_bias.unsqueeze(0)

      
        attn = self.softmax(attn)
        attn = self.attn_drop(attn)

        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x

    def extra_repr(self) -> str:
        #return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'
        return f'dim={self.dim}, num_heads={self.num_heads}'
    def flops(self, N):
        # calculate flops for 1 window with token length of N
        flops = 0
        # qkv = self.qkv(x)
        flops += N * self.dim * 3 * self.dim
        # attn = (q @ k.transpose(-2, -1))
        flops += self.num_heads * N * (self.dim // self.num_heads) * N
        #  x = (attn @ v)
        flops += self.num_heads * N * N * (self.dim // self.num_heads)
        # x = self.proj(x)
        flops += N * self.dim * self.dim
        return flops

class GlobalAttention(nn.Module):
    r""" MOA - multi-head self attention (W-MSA) module with relative position bias.

    Args:
        dim (int): Number of input channels.
        window_size (tuple[int]): The height and width of the window.
        num_heads (int): Number of attention heads.
        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
    """

    def __init__(self, dim, window_size, input_resolution,num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):

        super().__init__()
        self.dim = dim
        self.window_size = window_size  # Wh, Ww
        self.query_size = self.window_size[0]
       
        self.key_size = self.window_size[0] + 2
        h,w = input_resolution
        self.seq_len = h//self.query_size
    
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim ** -0.5
        self.reduction = 32
        self.pre_conv = nn.Conv2d(dim, int(dim//self.reduction), 1)
     
      
        # define a parameter table of relative position bias
        self.relative_position_bias_table = nn.Parameter(
            torch.zeros((2 * self.seq_len - 1) * (2 * self.seq_len - 1), num_heads))  # 2*Wh-1 * 2*Ww-1, nH
        #print(self.relative_position_bias_table.shape)
        # get pair-wise relative position index for each token inside the window
        coords_h = torch.arange(self.seq_len)
        coords_w = torch.arange(self.seq_len)
        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
     
        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
        
        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
        
        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2

        relative_coords[:, :, 0] += self.seq_len - 1  # shift to start from 0

        relative_coords[:, :, 1] += self.seq_len - 1
        relative_coords[:, :, 0] *= 2 * self.seq_len - 1
       
        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
      
        self.register_buffer("relative_position_index", relative_position_index)
        
       

        self.queryembedding = Rearrange('b c (h p1) (w p2) -> b (p1 p2 c) h w', p1 = self.query_size, p2 = self. query_size)

        self.keyembedding = nn.Unfold(kernel_size=(self.key_size, self.key_size), stride = 14, padding=1)
   
        self.query_dim = int(dim//self.reduction) * self.query_size * self.query_size
        self.key_dim = int(dim//self.reduction) * self.key_size * self.key_size
                
        self.q = nn.Linear(self.query_dim, self.dim,bias=qkv_bias)
        self.kv = nn.Linear(self.key_dim, 2*self.dim,bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim,dim)
        self.proj_drop = nn.Dropout(proj_drop)

        #trunc_normal_(self.relative_position_bias_table, std=.02)
        self.softmax = nn.Softmax(dim=-1)

    def forward(self, x, H, W):
        """
        Args:
            x: input features with shape of (num_windows*B, N, C)
            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
        """

        #B, H, W, C = x.shape
        B,_, C = x.shape  
          
        x = x.reshape(-1, C, H, W)    
        x = self.pre_conv(x)
        query = self.queryembedding(x).view(B,-1,self.query_dim)
        query = self.q(query)
        B,N,C = query.size()
        
        q = query.reshape(B,N,self.num_heads, C//self.num_heads).permute(0,2,1,3)
        key = self.keyembedding(x).view(B,-1,self.key_dim)
        kv = self.kv(key).reshape(B,N,2,self.num_heads,C//self.num_heads).permute(2,0,3,1,4)
        k = kv[0]
        v = kv[1]
        
        
        q = q * self.scale
        
        attn = (q @ k.transpose(-2, -1))

        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
            self.seq_len * self.seq_len, self.seq_len * self.seq_len, -1)  # Wh*Ww,Wh*Ww,nH
        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww

       
        attn = attn + relative_position_bias.unsqueeze(0)
      
        attn = self.softmax(attn)
        
        attn = self.attn_drop(attn)
     
        x = (attn @ v).transpose(1, 2).reshape(B, N, C) 
     
        x = self.proj(x)
      
        x = self.proj_drop(x)

        return x

    def extra_repr(self) -> str:
        return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'

    def flops(self, N):
        # calculate flops for 1 window with token length of N
        flops = 0
        # qkv = self.qkv(x)
        flops += N * self.dim * 3 * self.dim
        # attn = (q @ k.transpose(-2, -1))
        flops += self.num_heads * N * (self.dim // self.num_heads) * N
        #  x = (attn @ v)
        flops += self.num_heads * N * N * (self.dim // self.num_heads)
        # x = self.proj(x)
        flops += N * self.dim * self.dim
        return flops


class LocalTransformerBlock(nn.Module):
    r""" Local Transformer Block.

    Args:
        dim (int): Number of input channels.
        input_resolution (tuple[int]): Input resulotion.
        num_heads (int): Number of attention heads.
        window_size (int): Window size.
        shift_size (int): Shift size for SW-MSA.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
        drop (float, optional): Dropout rate. Default: 0.0
        attn_drop (float, optional): Attention dropout rate. Default: 0.0
        drop_path (float, optional): Stochastic depth rate. Default: 0.0
        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
    """

    def __init__(self, dim, input_resolution, num_heads, window_size=7,
                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
                 act_layer=nn.GELU, norm_layer=nn.LayerNorm):
        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.num_heads = num_heads
        self.window_size = window_size
        self.mlp_ratio = mlp_ratio
   
        if min(self.input_resolution) <= self.window_size:
            # if window size is larger than input resolution, we don't partition windows
           
            self.window_size = min(self.input_resolution)
   
       
        self.norm1 = norm_layer(dim)
        self.attn = WindowAttention(
            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
            qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)

        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)



    def forward(self, x):
        H, W = self.input_resolution
     
        B, L, C = x.shape
      
        assert L == H * W, "input feature has wrong size"
       
        shortcut = x
        x = self.norm1(x)
        
        x = x.view(B, H, W, C)
     

    
        x_windows = window_partition(x, self.window_size)  # nW*B, window_size, window_size, C 
        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C     
        attn_windows = self.attn(x_windows)  # nW*B, window_size*window_size, C    
        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
        x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C
        x = x.view(B, H * W, C)


       

 
        x = shortcut + self.drop_path(x)
        x = x + self.drop_path(self.mlp(self.norm2(x)))
        return x

    def extra_repr(self) -> str:
        return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
               f"window_size={self.window_size}, mlp_ratio={self.mlp_ratio}"

    def flops(self):
        flops = 0
        H, W = self.input_resolution
        # norm1
        flops += self.dim * H * W
        # W-MSA/SW-MSA
        nW = H * W / self.window_size / self.window_size
        flops += nW * self.attn.flops(self.window_size * self.window_size)
        # mlp
        flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
        # norm2
        flops += self.dim * H * W
        return flops


class PatchMerging(nn.Module):
    """ Patch Merging Layer.

    Args:
        input_resolution (tuple[int]): Resolution of input feature.
        dim (int): Number of input channels.
        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
    """

    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
        super().__init__()
        self.input_resolution = input_resolution
        self.dim = dim
        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
        self.norm = norm_layer(4 * dim)

    def forward(self, x):
        """
        x: B, H*W, C
        """
        H, W = self.input_resolution
        B, L, C = x.shape
        assert L == H * W, "input feature has wrong size"
        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."

        x = x.view(B, H, W, C)

        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C

        x = self.norm(x)
        x = self.reduction(x)

        return x

    def extra_repr(self) -> str:
        return f"input_resolution={self.input_resolution}, dim={self.dim}"

    def flops(self):
        H, W = self.input_resolution
        flops = H * W * self.dim
        flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
        return flops


class BasicLayer(nn.Module):
    """ A basic Swin Transformer layer for one stage.

    Args:
        dim (int): Number of input channels.
        input_resolution (tuple[int]): Input resolution.
        depth (int): Number of blocks.
        num_heads (int): Number of attention heads.
        window_size (int): Local window size.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
        drop (float, optional): Dropout rate. Default: 0.0
        attn_drop (float, optional): Attention dropout rate. Default: 0.0
        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
    """

    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, drop_path_global=0., use_checkpoint=False):

        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.depth = depth
        self.use_checkpoint = use_checkpoint
        self.window_size = window_size
       
        self.drop_path_gl = DropPath(drop_path_global) if drop_path_global > 0. else nn.Identity()
        # build blocks
        self.blocks = nn.ModuleList([
            LocalTransformerBlock(dim=dim, input_resolution=input_resolution,
                                 num_heads=num_heads, window_size=window_size,
                                 mlp_ratio=mlp_ratio,
                                 qkv_bias=qkv_bias, qk_scale=qk_scale,
                                 drop=drop, attn_drop=attn_drop,
                                 drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
                                 norm_layer=norm_layer)
            for i in range(depth)])

        # patch merging layer
        if downsample is not None:
           if min(self.input_resolution) >= self.window_size:
                 self.glb_attn = GlobalAttention(dim, to_2tuple(window_size), self.input_resolution, num_heads = num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
                 self.post_conv = nn.Conv2d(dim, dim, 3, padding=1)
                 self.norm1 = norm_layer(dim)
                 self.norm2 = norm_layer(dim)
                
           else:
                 self.post_conv = None
                 self.glb_attn = None
                 self.norm1 = None
                 self.norm2 = None
                
           self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
           
            
        else:
            self.downsample = None
            
    def forward(self, x):
        for blk in self.blocks:
            if self.use_checkpoint:
                x = checkpoint.checkpoint(blk, x)
            else:
                x = blk(x)
                
        if self.downsample is not None:
        
           if min(self.input_resolution) >= self.window_size:
                shortcut = x
                x = self.norm1(x)
                H, W = self.input_resolution
                B,_,C = x.size()
         
                no_window = int(H*W/self.window_size**2)   
                local_attn = x.view(B,no_window,self.window_size, self.window_size,C)
             
                glb_attn = self.glb_attn(x, H, W)
                glb_attn = glb_attn.view(B,no_window,1,1,C)
                x = torch.add(local_attn, glb_attn).view(B,C,H,W)
              

                x = shortcut.view(B,C,H,W) + self.drop_path_gl(x)
                x = self.norm2(x.view(B,H*W,C))
                post_conv = self.drop_path_gl(self.post_conv(x.view(B,C,H,W))).view(B, H*W, C)
                x = x + post_conv
                
           x = self.downsample(x)
        return x

    def extra_repr(self) -> str:
        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"

    def flops(self):
        flops = 0
        for blk in self.blocks:
            flops += blk.flops()
        if self.downsample is not None:
            flops += self.downsample.flops()
        return flops


class PatchEmbed(nn.Module):
    r""" Image to Patch Embedding

    Args:
        img_size (int): Image size.  Default: 224.
        patch_size (int): Patch token size. Default: 4.
        in_chans (int): Number of input image channels. Default: 3.
        embed_dim (int): Number of linear projection output channels. Default: 96.
        norm_layer (nn.Module, optional): Normalization layer. Default: None
    """

    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
        super().__init__()
        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)
        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
        self.img_size = img_size
        self.patch_size = patch_size
        self.patches_resolution = patches_resolution
        self.num_patches = patches_resolution[0] * patches_resolution[1]

        self.in_chans = in_chans
        self.embed_dim = embed_dim

        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
        if norm_layer is not None:
            self.norm = norm_layer(embed_dim)
        else:
            self.norm = None

    def forward(self, x):
        B, C, H, W = x.shape
        # FIXME look at relaxing size constraints
        assert H == self.img_size[0] and W == self.img_size[1], \
            f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
        x = self.proj(x).flatten(2).transpose(1, 2)  # B Ph*Pw C
        if self.norm is not None:
            x = self.norm(x)
        return x

    def flops(self):
        Ho, Wo = self.patches_resolution
        flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
        if self.norm is not None:
            flops += Ho * Wo * self.embed_dim
        return flops


class MOATransformer(nn.Module):
    r""" Swin Transformer
        A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -
          https://arxiv.org/pdf/2103.14030

    Args:
        img_size (int | tuple(int)): Input image size. Default 224
        patch_size (int | tuple(int)): Patch size. Default: 4
        in_chans (int): Number of input image channels. Default: 3
        num_classes (int): Number of classes for classification head. Default: 1000
        embed_dim (int): Patch embedding dimension. Default: 96
        depths (tuple(int)): Depth of each Swin Transformer layer.
        num_heads (tuple(int)): Number of attention heads in different layers.
        window_size (int): Window size. Default: 7
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
        drop_rate (float): Dropout rate. Default: 0
        attn_drop_rate (float): Attention dropout rate. Default: 0
        drop_path_rate (float): Stochastic depth rate. Default: 0.1
        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
        patch_norm (bool): If True, add normalization after patch embedding. Default: True
        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
    """

    def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes=1000,
                 embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24],
                 window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,
                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
                 norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
                 use_checkpoint=False, **kwargs):
        super().__init__()

        self.num_classes = num_classes
        self.num_layers = len(depths)
        self.embed_dim = embed_dim
        self.ape = ape
        self.patch_norm = patch_norm
        self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
        self.mlp_ratio = mlp_ratio

        # split image into non-overlapping patches
        self.patch_embed = PatchEmbed(
            img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,
            norm_layer=norm_layer if self.patch_norm else None)
        num_patches = self.patch_embed.num_patches
        patches_resolution = self.patch_embed.patches_resolution
        self.patches_resolution = patches_resolution

        # absolute position embedding
        if self.ape:
            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
            trunc_normal_(self.absolute_pos_embed, std=.02)

        self.pos_drop = nn.Dropout(p=drop_rate)

        # stochastic depth
        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
        dpr_global = [x.item() for x in torch.linspace(0, 0.2, len(depths)-1)]
        # build layers
        self.layers = nn.ModuleList()
        for i_layer in range(self.num_layers):
            layer = BasicLayer(dim=int(embed_dim * 2 ** i_layer),
                               input_resolution=(patches_resolution[0] // (2 ** i_layer),
                                                 patches_resolution[1] // (2 ** i_layer)),
                               depth=depths[i_layer],
                               num_heads=num_heads[i_layer],
                               window_size=window_size,
                               mlp_ratio=self.mlp_ratio,
                               qkv_bias=qkv_bias, qk_scale=qk_scale,
                               drop=drop_rate, attn_drop=attn_drop_rate,
                               drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
                               norm_layer=norm_layer,
                               downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
                               drop_path_global = (dpr_global[i_layer]) if (i_layer < self.num_layers -1) else 0,
                               use_checkpoint=use_checkpoint)
            self.layers.append(layer)

        self.norm = norm_layer(self.num_features)
        self.avgpool = nn.AdaptiveAvgPool1d(1)
        self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()

        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)

    @torch.jit.ignore
    def no_weight_decay(self):
        return {'absolute_pos_embed'}

    @torch.jit.ignore
    def no_weight_decay_keywords(self):
        return {'relative_position_bias_table'}

    def forward_features(self, x):
        x = self.patch_embed(x)
        if self.ape:
            x = x + self.absolute_pos_embed
        x = self.pos_drop(x)
 
        for layer in self.layers:
            x = layer(x)
           
        x = self.norm(x)  # B L C
        x = self.avgpool(x.transpose(1, 2))  # B C 1
        x = torch.flatten(x, 1)
        return x

    def forward(self, x):
        x = self.forward_features(x)
        x = self.head(x)
        return x

    def flops(self):
        flops = 0
        flops += self.patch_embed.flops()
        for i, layer in enumerate(self.layers):
            flops += layer.flops()
        flops += self.num_features * self.patches_resolution[0] * self.patches_resolution[1] // (2 ** self.num_layers)
        flops += self.num_features * self.num_classes
        return flops

文章来源:https://blog.csdn.net/qq_40035462/article/details/134974989
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